Instructions to use qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M

Ollama
How to use qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF with Ollama:
```
ollama run hf.co/qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF with Docker Model Runner:
```
docker model run hf.co/qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M
```

Lemonade

How to use qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.mcp-tool-use-quality-ranger-0.6b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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mcp-tool-use-quality-ranger-0.6b-GGUF

mcp-tool-use-quality-ranger-0.6b is a sequence classification model fine-tuned from Qwen3-0.6B-Base, designed to evaluate the quality of function calls within conversational AI systems using the Model Context Protocol (MCP) framework. Supporting a context length of 32,768 tokens, it classifies function calls as VALID_CALL, TOOL_ERROR, PARAM_NAME_ERROR, or PARAM_VALUE_ERROR by verifying tool selection, parameter names, and parameter values, and delivers robust, fast assessments for dialog-based tool usage, parameter errors, and value correctness. Optimized for lightweight deployment, mcp-tool-use-quality-ranger-0.6b achieves high benchmark accuracy, making it ideal for developers and researchers who require precise tool call evaluations in AI workflows.

Model Files

File Name	Quant Type	File Size
mcp-tool-use-quality-ranger-0.6b.BF16.gguf	BF16	1.2 GB
mcp-tool-use-quality-ranger-0.6b.F16.gguf	F16	1.2 GB
mcp-tool-use-quality-ranger-0.6b.F32.gguf	F32	2.39 GB
mcp-tool-use-quality-ranger-0.6b.Q2_K.gguf	Q2_K	296 MB
mcp-tool-use-quality-ranger-0.6b.Q3_K_L.gguf	Q3_K_L	368 MB
mcp-tool-use-quality-ranger-0.6b.Q3_K_M.gguf	Q3_K_M	347 MB
mcp-tool-use-quality-ranger-0.6b.Q3_K_S.gguf	Q3_K_S	323 MB
mcp-tool-use-quality-ranger-0.6b.Q4_0.gguf	Q4_0	382 MB
mcp-tool-use-quality-ranger-0.6b.Q4_1.gguf	Q4_1	409 MB
mcp-tool-use-quality-ranger-0.6b.Q4_K.gguf	Q4_K	397 MB
mcp-tool-use-quality-ranger-0.6b.Q4_K_M.gguf	Q4_K_M	397 MB
mcp-tool-use-quality-ranger-0.6b.Q4_K_S.gguf	Q4_K_S	383 MB
mcp-tool-use-quality-ranger-0.6b.Q5_0.gguf	Q5_0	437 MB
mcp-tool-use-quality-ranger-0.6b.Q5_1.gguf	Q5_1	464 MB
mcp-tool-use-quality-ranger-0.6b.Q5_K.gguf	Q5_K	444 MB
mcp-tool-use-quality-ranger-0.6b.Q5_K_M.gguf	Q5_K_M	444 MB
mcp-tool-use-quality-ranger-0.6b.Q5_K_S.gguf	Q5_K_S	437 MB
mcp-tool-use-quality-ranger-0.6b.Q6_K.gguf	Q6_K	495 MB
mcp-tool-use-quality-ranger-0.6b.Q8_0.gguf	Q8_0	639 MB

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 11

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for qualifire-oss/mcp-tool-use-quality-ranger-0.6b-GGUF

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

rogue-security/mcp-tool-use-quality-ranger-0.6b

Quantized

(2)

this model